ROBUST IDENTIFICATION OF PROCESS MODELS FROM PLANT DATA

Robust Induction of Process Models from Time-Series Data

In this paper, we revisit the problem of inducing a process model from time-series data. We illustrate this task with a realistic ecosystem model, review an initial method for its induction, then identify three challenges that require extension of this method. These include dealing with unobservable variables, finding numeric conditions on processes, and preventing the creation of models that o...

Latent Autoregressive Gaussian Process Models for Robust System Identification

We introduce GP-RLARX, a novel Gaussian Process (GP) model for robust system identification. Our approach draws inspiration from nonlinear autoregressive modeling with exogenous inputs (NARX) and it encapsulates a novel and powerful structure referred to as latent autoregression. This structure accounts for the feedback of uncertain values during training and provides a natural framework for fr...

From Plant Data to Process Control : Ideas for Process

Verification of Process Plant Models

The motivation for creating and verifying process plant models is briefly described. Verification is defined. Verification techniques for signed directed graphs models of process plants are detailed. Two case studies are presented to evaluate the verification techniques. A limitation with the verification is detailed.

Weak process models for robust process detection

Many defense and security applications involve the detection of a dynamic process. A process model describes the state transitions of an object, which evolves in time according to specific know laws. Given a process model, the process detection problem is to identify the existence of such a process in large amount of observation data. While Hidden Markov Models (HMMs) are widely used to charact...